Controlexperient
Read and preprocess control-data
if (!require("pacman")) install.packages("pacman")
library(pacman)
p_load(tidyverse)
p_load(R.utils)
sourceDirectory("./source/")
data<-readControl() %>% preproControl() check accuracy
corrResp: correct response rtResp: reaction time of response - inSet: 0 - correct rejection (true negativ) - inSet: 1 - Hit (true positiv) - session: 1-5 - time factor (not constant, therefore only ordinal) - kategory: Landscape, Face, Art
p_load(dplyr)
p_load(brms)
options (mc.cores=parallel::detectCores ()) # Run on multiple cores
p_load(sjPlot)
group_by(data,kategory,session) %>%
summarize(mean_corr= mean(corrResp),sd_corr=sd(corrResp),mean_RT=mean(rtResp),sd_RT=sd(rtResp))p <- ggplot(data=data,
aes(x = corrResp, y =rtResp, color= inSet) )+geom_point()
p+facet_grid(kategory ~ session,margins = T)
p+facet_grid(kategory ~ participant)
#correct response
fit_corrRespSessionPart<-lm(corrResp~participant*session,data = data)
summary(fit_corrRespSessionPart)##
## Call:
## lm(formula = corrResp ~ participant * session, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7667 -0.5667 0.3333 0.4000 0.5000
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.000e-01 8.994e-02 6.671 6.48e-11 ***
## participantha94 -5.432e-15 1.272e-01 0.000 1.000
## participantma92 -6.667e-02 1.272e-01 -0.524 0.600
## participantod94 3.333e-02 1.272e-01 0.262 0.793
## participantug93 6.667e-02 1.272e-01 0.524 0.600
## session2 2.925e-15 1.272e-01 0.000 1.000
## session3 3.333e-02 1.272e-01 0.262 0.793
## session4 -6.667e-02 1.272e-01 -0.524 0.600
## session5 -6.667e-02 1.272e-01 -0.524 0.600
## participantha94:session2 -3.333e-02 1.799e-01 -0.185 0.853
## participantma92:session2 2.333e-01 1.799e-01 1.297 0.195
## participantod94:session2 -1.333e-01 1.799e-01 -0.741 0.459
## participantug93:session2 NA NA NA NA
## participantha94:session3 -3.333e-02 1.799e-01 -0.185 0.853
## participantma92:session3 3.333e-02 1.799e-01 0.185 0.853
## participantod94:session3 -1.000e-01 1.799e-01 -0.556 0.578
## participantug93:session3 NA NA NA NA
## participantha94:session4 NA NA NA NA
## participantma92:session4 6.667e-02 1.799e-01 0.371 0.711
## participantod94:session4 6.667e-02 1.799e-01 0.371 0.711
## participantug93:session4 NA NA NA NA
## participantha94:session5 NA NA NA NA
## participantma92:session5 1.000e-01 1.799e-01 0.556 0.578
## participantod94:session5 NA NA NA NA
## participantug93:session5 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4926 on 522 degrees of freedom
## Multiple R-squared: 0.01634, Adjusted R-squared: -0.0157
## F-statistic: 0.51 on 17 and 522 DF, p-value: 0.9486sjp.int(fit = fit_corrRespSessionPart, type = "eff")
#correct response
fit_corrRespSession<-lm(corrResp~session*kategory,data = data)
summary(fit_corrRespSession)##
## Call:
## lm(formula = corrResp ~ session * kategory, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8500 -0.5667 0.3250 0.4000 0.6000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.000e-01 6.894e-02 7.253 1.47e-12 ***
## session2 1.000e-01 1.034e-01 0.967 0.3340
## session3 5.000e-02 1.034e-01 0.484 0.6289
## session4 6.667e-02 1.126e-01 0.592 0.5540
## session5 -1.000e-01 1.290e-01 -0.775 0.4385
## kategoryFace 2.200e-01 9.749e-02 2.257 0.0244 *
## kategoryLandscape 1.000e-01 9.749e-02 1.026 0.3055
## session2:kategoryFace -1.700e-01 1.462e-01 -1.162 0.2456
## session3:kategoryFace -1.450e-01 1.462e-01 -0.992 0.3219
## session4:kategoryFace -2.200e-01 1.592e-01 -1.382 0.1676
## session5:kategoryFace 2.300e-01 1.824e-01 1.261 0.2079
## session2:kategoryLandscape -7.500e-02 1.462e-01 -0.513 0.6083
## session3:kategoryLandscape 2.500e-02 1.462e-01 0.171 0.8643
## session4:kategoryLandscape -3.333e-02 1.592e-01 -0.209 0.8342
## session5:kategoryLandscape 9.970e-16 1.824e-01 0.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4875 on 525 degrees of freedom
## Multiple R-squared: 0.03118, Adjusted R-squared: 0.005347
## F-statistic: 1.207 on 14 and 525 DF, p-value: 0.2659sjp.lm(fit = fit_corrRespSession)
fit_corrRespSet<-lm(corrResp~kategory*inSet,data = data)
summary(fit_corrRespSet)##
## Call:
## lm(formula = corrResp ~ kategory * inSet, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8889 -0.3444 0.1111 0.2222 0.7556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82222 0.04484 18.335 < 2e-16 ***
## kategoryFace -0.04444 0.06342 -0.701 0.484
## kategoryLandscape -0.47778 0.06342 -7.534 2.12e-13 ***
## inSet1 -0.57778 0.06342 -9.111 < 2e-16 ***
## kategoryFace:inSet1 0.36667 0.08969 4.088 5.01e-05 ***
## kategoryLandscape:inSet1 1.12222 0.08969 12.513 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4254 on 534 degrees of freedom
## Multiple R-squared: 0.2495, Adjusted R-squared: 0.2425
## F-statistic: 35.5 on 5 and 534 DF, p-value: < 2.2e-16sjp.int(fit = fit_corrRespSet, type = "eff")
fit_corrRespParticipant<-lm(corrResp~kategory*participant,data = data)
summary(fit_corrRespParticipant)##
## Call:
## lm(formula = corrResp ~ kategory * participant, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.78 -0.54 0.34 0.38 0.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.000e-01 1.546e-01 3.235 0.00129
## kategoryFace 2.000e-01 2.186e-01 0.915 0.36059
## kategoryLandscape 1.000e-01 2.186e-01 0.458 0.64749
## participantha94 -1.811e-16 1.785e-01 0.000 1.00000
## participantma92 4.000e-02 1.693e-01 0.236 0.81332
## participantod94 1.000e-01 1.728e-01 0.579 0.56302
## participantug93 1.532e-15 1.693e-01 0.000 1.00000
## kategoryFace:participantha94 -6.667e-02 2.524e-01 -0.264 0.79177
## kategoryLandscape:participantha94 3.333e-02 2.524e-01 0.132 0.89498
## kategoryFace:participantma92 -1.200e-01 2.394e-01 -0.501 0.61645
## kategoryLandscape:participantma92 -3.139e-15 2.394e-01 0.000 1.00000
## kategoryFace:participantod94 -1.750e-01 2.444e-01 -0.716 0.47423
## kategoryLandscape:participantod94 -1.750e-01 2.444e-01 -0.716 0.47423
## kategoryFace:participantug93 8.000e-02 2.394e-01 0.334 0.73842
## kategoryLandscape:participantug93 6.000e-02 2.394e-01 0.251 0.80223
##
## (Intercept) **
## kategoryFace
## kategoryLandscape
## participantha94
## participantma92
## participantod94
## participantug93
## kategoryFace:participantha94
## kategoryLandscape:participantha94
## kategoryFace:participantma92
## kategoryLandscape:participantma92
## kategoryFace:participantod94
## kategoryLandscape:participantod94
## kategoryFace:participantug93
## kategoryLandscape:participantug93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4887 on 525 degrees of freedom
## Multiple R-squared: 0.02615, Adjusted R-squared: 0.0001782
## F-statistic: 1.007 on 14 and 525 DF, p-value: 0.4446sjp.int(fit = fit_corrRespParticipant, type = "eff")## `sjp.int()` will become deprecated in the future. Please use `plot_model()` instead.
# reaction time
fit_rtResp<-lm(rtResp~kategory*inSet, data)
summary(fit_rtResp)##
## Call:
## lm(formula = rtResp ~ kategory * inSet, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8753 -0.4474 -0.1997 0.1549 6.1917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.99808 0.08058 12.386 <2e-16 ***
## kategoryFace -0.18165 0.11396 -1.594 0.1115
## kategoryLandscape 0.02714 0.11396 0.238 0.8119
## inSet1 0.06191 0.11396 0.543 0.5872
## kategoryFace:inSet1 0.01076 0.16117 0.067 0.9468
## kategoryLandscape:inSet1 -0.40261 0.16117 -2.498 0.0128 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7645 on 534 degrees of freedom
## Multiple R-squared: 0.02899, Adjusted R-squared: 0.0199
## F-statistic: 3.189 on 5 and 534 DF, p-value: 0.00758sjp.int(fit = fit_rtResp,type = "eff")
fit_rtRespSession<-lm(rtResp~kategory*session, data)
summary(fit_rtRespSession)##
## Call:
## lm(formula = rtResp ~ kategory * session, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2768 -0.3883 -0.1710 0.1836 5.6407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.61099 0.10218 15.766 < 2e-16 ***
## kategoryFace -0.30035 0.14451 -2.078 0.03816 *
## kategoryLandscape -0.62994 0.14451 -4.359 1.57e-05 ***
## session2 -0.66607 0.15327 -4.346 1.67e-05 ***
## session3 -0.83591 0.15327 -5.454 7.61e-08 ***
## session4 -0.96737 0.16686 -5.797 1.16e-08 ***
## session5 -0.78261 0.19117 -4.094 4.91e-05 ***
## kategoryFace:session2 0.17388 0.21676 0.802 0.42281
## kategoryLandscape:session2 0.57210 0.21676 2.639 0.00856 **
## kategoryFace:session3 0.08805 0.21676 0.406 0.68477
## kategoryLandscape:session3 0.63104 0.21676 2.911 0.00375 **
## kategoryFace:session4 0.23571 0.23598 0.999 0.31832
## kategoryLandscape:session4 0.64841 0.23598 2.748 0.00621 **
## kategoryFace:session5 0.23928 0.27035 0.885 0.37652
## kategoryLandscape:session5 0.72305 0.27035 2.674 0.00772 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7225 on 525 degrees of freedom
## Multiple R-squared: 0.1472, Adjusted R-squared: 0.1245
## F-statistic: 6.474 on 14 and 525 DF, p-value: 4.012e-12sjp.int(fit = fit_rtRespSession,type = "eff")
fit_rtRespParticipant<-lm(rtResp~kategory*participant, data)
summary(fit_rtRespParticipant)##
## Call:
## lm(formula = rtResp ~ kategory * participant, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2239 -0.3735 -0.1479 0.1973 4.1939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0577 0.2096 14.592 < 2e-16 ***
## kategoryFace -0.8271 0.2964 -2.791 0.005449 **
## kategoryLandscape -1.3568 0.2964 -4.578 5.86e-06 ***
## participantha94 -2.2891 0.2420 -9.460 < 2e-16 ***
## participantma92 -2.3854 0.2296 -10.391 < 2e-16 ***
## participantod94 -1.9848 0.2343 -8.472 2.44e-16 ***
## participantug93 -1.9566 0.2296 -8.524 < 2e-16 ***
## kategoryFace:participantha94 0.7492 0.3422 2.189 0.029014 *
## kategoryLandscape:participantha94 1.3393 0.3422 3.914 0.000103 ***
## kategoryFace:participantma92 0.7064 0.3246 2.176 0.029994 *
## kategoryLandscape:participantma92 1.2838 0.3246 3.955 8.71e-05 ***
## kategoryFace:participantod94 0.6676 0.3313 2.015 0.044434 *
## kategoryLandscape:participantod94 1.0916 0.3313 3.295 0.001052 **
## kategoryFace:participantug93 0.6529 0.3246 2.011 0.044830 *
## kategoryLandscape:participantug93 1.2967 0.3246 3.994 7.41e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6627 on 525 degrees of freedom
## Multiple R-squared: 0.2827, Adjusted R-squared: 0.2636
## F-statistic: 14.78 on 14 and 525 DF, p-value: < 2.2e-16sjp.int(fit = fit_rtRespParticipant,type = "eff")
if(T){
fitBrmCorrResp<-brm(corrResp~inSet*kategory+(1|session)+(1|participant)+(1|id),data = data,family = bernoulli())
summary(fitBrmCorrResp)
marginal_effects(fitBrmCorrResp)
plot(fitBrmCorrResp)
fitBrmRt<-brm(rtResp~inSet*kategory+(1|session)+(1|participant)+(1|id),data = data,family =exgaussian(link = "identity", link_sigma = "log", link_beta = "log"))
summary(fitBrmRt)
marginal_effects(fitBrmRt)
plot(fitBrmRt)
}## Compiling the C++ model## Start sampling
## Compiling the C++ model
## Start sampling
